Implied volatility forecast and option trading strategy

2021 ◽  
Vol 71 ◽  
pp. 943-954
Author(s):  
Dehong Liu ◽  
Yucong Liang ◽  
Lili Zhang ◽  
Peter Lung ◽  
Rizwan Ullah
2020 ◽  
Vol 4 (2) ◽  
pp. 111-127
Author(s):  
Pierre Rostan ◽  
Alexandra Rostan ◽  
Mohammad Nurunnabi

Purpose The purpose of this paper is to illustrate a profitable and original index options trading strategy. Design/methodology/approach The methodology is based on auto regressive integrated moving average (ARIMA) forecasting of the S&P 500 index and the strategy is tested on a large database of S&P 500 Composite index options and benchmarked to the generalized auto regressive conditional heteroscedastic (GARCH) model. The forecasts validate a set of criteria as follows: the first criterion checks if the forecasted index is greater or lower than the option strike price and the second criterion if the option premium is underpriced or overpriced. A buy or sell and hold strategy is finally implemented. Findings The paper demonstrates the valuable contribution of this option trading strategy when trading call and put index options. It especially demonstrates that the ARIMA forecasting method is a valid method for forecasting the S&P 500 Composite index and is superior to the GARCH model in the context of an application to index options trading. Originality/value The strategy was applied in the aftermath of the 2008 credit crisis over 60 months when the volatility index (VIX) was experiencing a downtrend. The strategy was successful with puts and calls traded on the USA market. The strategy may have a different outcome in a different economic and regional context.


2011 ◽  
Vol 19 (3) ◽  
pp. 251-280
Author(s):  
Byungwook Choi

This study investigates a forecasting power of volatility curvatures and risk neutral densities implicit in KOSPI 200 option prices by analyzing minute by minute historical index option intraday trading data from January of 2007 to January of 2011. We begin by estimating implied volatility functions and risk neutral price densities based on non-parametric method every minute and by calculating volatility curvature and skewness premium. We then compare the daily rate of return of the signal following trading strategy that we buy (sell) a stock index when the volatility curvature or skewness premium increases (decreases) with that of an intraday buy-and-hold strategy that we buy a stock index on 9:05AM and sell it on 2:50PM. We found that the rate of return of the signal following trading strategy was significantly higher than that of the intraday buy-and-hold strategy, which implies that the option prices have a strong forecasting power on the direction of stock market. Another finding is that the information contents of option prices disappear after three or four minutes.


2017 ◽  
Vol 5 (9) ◽  
pp. 41
Author(s):  
Guillermo Benavides

There has been substantial research effort aimed to forecast futures price return volatilities of financial assets. A significant part of the literature shows that volatility forecast accuracy is not easy to estimate regardless of the forecasting model applied. This paper examines the volatility accuracy of several volatility forecast models for the case of the Mexican peso-USD exchange rate futures returns. The models applied here are a univariate GARCH, a multivariate ARCH (the BEKK model), two option implied volatility models and a composite forecast model. The composite model includes time-series (historical) and option implied volatility forecasts. Different to other works in the literature, in this paper there is a more rigorous analysis of the option implied volatilities calculations. The results show that the option implied models are superior to the historical models in terms of accuracy and that the composite forecast model was the most accurate one (compared to the alternative models) having the lowest mean-squared-errors. However, the results should be taken with caution given that the coefficient of determination in the regressions was relatively low. According to these findings it is recommended to use a composite forecast model if both types of data are available i.e. the time-series (historical) and the option implied.


2021 ◽  
Vol 10 (1) ◽  
pp. 166-203
Author(s):  
Alexander Brunhuemer ◽  
Gerhard Larcher ◽  
Lukas Larcher

In this paper, we examine the performance of certain short option trading strategies on the S&P500 with backtesting based on historical option price data. Some of these strategies show significant outperformance in relation to the S&P500 index. We seek to explain this outperformance by modeling the negative correlation between the S&P500 and its implied volatility (given by the VIX) and through Monte Carlo simulation. We also provide free testing software and give an introduction to its use for readers interested in running further backtests on their own.


Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1026 ◽  
Author(s):  
Haibin Yan ◽  
Ping-An Zhong ◽  
Juan Chen ◽  
Bin Xu ◽  
Yenan Wu ◽  
...  

The uncertainty of forecasted runoffs brings risks of water shortages to water users in the intake area of long-distance water transfer projects, and the uncertainty of spot market prices may cause them to buy water at high prices. In order to hedge these risks, this paper proposes a risk hedging model for decision-making in water option trading from the viewpoint of water users. With the objective of maximizing the expected revenue of water users, the proposed model was solved by an analytical method and an optimal water option strategy was obtained for the users. The proposed model is applied to an intake area of an inter-basin water transfer project in China. The results show that the proposed water option trading model can provide water users with an optimal option strategy. The optimal options trading strategy can effectively reduce the risk caused by the uncertainties of forecasted runoffs and water prices. We also explored the influence of the uncertainty degree of the forecasted runoffs and water price on the option trading strategy. The results show that the expected revenue of water users increases as the variances of the errors of forecasted runoffs and water prices increase.


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